Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks

Abstract

Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model.

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Cited by in corpus (7)

• Detecting and interpreting myocardial infarction using fully convolutional neural networks
• Deep Learning in Cardiology
• Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
• Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
• Sparsely Activated Networks: A new method for decomposing and compressing data
• DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time
• SIM-ECG: A Signal Importance Mask-driven ECGClassification System